PROJECT 1 - LAWRENCE BERKELEY LAB - PHENIX

项目 1 - 劳伦斯伯克利实验室 - 凤凰城

• 批准号: 7208309
• 负责人: PAUL Damien ADAMS
• 金额: $ 70.88万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7208309
• 关键词: X ray crystallography; bioengineering /biomedical engineering; biomedical automation; computer human interaction; computer program /software; data management; functional /structural genomics; macromolecule; mathematical model; model design /development; structural biology

Project I: During the first 5 years of the PHENIX project we have implemented a program for automated analysis of crystallographic data (mmtbx.xtriage), we have developed a highly automated program for anomalous substructure location (HySS), and we have created a new automated structure refinement program (phenix.refine). We have played an essential role in the birth of PHENIX by developing the open source C++ Computational Crystallography Toolbox (cctbx) and the PHENIX graphical user interface (GUI). We have worked closely with the other members of the PHENIX project to create an integrated system for automated structure determination that provides command-line interfaces, a visual programming interface, and the highly automated Wizard interface. We will use our strong foundation of computational algorithms and software to address some of the most challenging bottlenecks remaining in structure determination; classification of experimental data for decisionmaking, substructure location from weak signals, and productive and minimally-biased structure refinement at all resolution ranges. Our new algorithms which automatically assign a probability score to each dataset that describes the likelihood of successful structure solution will be used in subsequent decision-making by Projects II and IV. The result will be an increase in the efficiency of structure solution. The chances of successful substructure location will be improved by new algorithms for automated space group determination, optimal data cutoff calculation, and the application of improved likelihood scoring functions, developed in collaboration with Project III. This unique capability will make it possible for PHENIX to successfully-begin the process of structure solution, where other systems currently fail. We will develop new algorithms, including the use of normal models, for the refinement of models against experimental data at any resolution limit, by using automatic model parameterizations that maintain a reasonable ratio of parameters to observed data. We will also extend structure refinement to include algorithms for the local rebuilding of models, including peptide flips and rotamer refitting, in collaboration with Projects II and V. This approach will speed the convergence of refinement and reduce the need for manual intervention in many cases. Finally, we will enhance the PHENIX software by further development of our cctbx library, the PHENIX GUI, Project Data Storage, and the developer environment. Our new algorithms will enable researchers to determine the structures of challenging, high-value biological macromolecules which are important for understanding biology and ultimately improving human health.

项目一：在PHENIX项目的前5年，我们实施了一个自动化的项目， 晶体学数据的分析（mmtbx.xtriage），我们已经开发了一个高度自动化的程序， 异常子结构位置（HySS），我们已经创建了一个新的自动结构细化 程序（phenix.refine）。我们在PHENIX的诞生中发挥了重要作用， 源C++计算晶体学（cctbx）和PHENIX图形用户界面（GUI）。 我们与PHENIX项目的其他成员密切合作，创建了一个集成系统， 提供命令行界面的自动结构确定，可视化编程界面， 和高度自动化的向导界面。 我们将利用我们强大的计算算法和软件基础来解决一些最重要的问题。 挑战结构确定中剩余的瓶颈;用于决策的实验数据的分类， 从弱信号中定位子结构，以及高效和最小偏差的结构细化 在所有分辨率范围。我们的新算法自动为每个数据集分配概率得分 描述成功结构解决方案的可能性将用于后续决策， 项目二和四。其结果将是提高结构解决方案的效率。的机会 成功的子结构定位将通过用于自动空间分组的新算法来改进 确定，最佳数据截止计算，以及应用改进的似然评分函数， 与项目III合作开发。这种独特的能力将使PHENIX能够 成功地开始结构化解决方案的过程，其他系统目前失败。发展新型 算法，包括正常模型的使用，用于在任何情况下针对实验数据改进模型。 分辨率限制，通过使用自动模型参数化，保持合理的参数比例 观察到的数据。我们还将扩展结构细化，以包括局部重建的算法， 模型，包括肽翻转和旋转异构体改装，与项目II和V合作，这种方法将 在许多情况下，加快细化的收敛并减少手动干预的需要。最后， 我们将通过进一步开发我们的cctbx库、PHENIX GUI、Project 数据存储和开发人员环境。 我们的新算法将使研究人员能够确定具有挑战性的，高价值的生物结构， 这些大分子对于理解生物学和最终改善人类健康非常重要。